Wearables & Capture

Headstone — LifeLine Data Layer

Document Version: 1.0

Research Date: March 2026

Status: Founding Reference Document

*"The richest life record ever assembled isn't in a hospital. It's scattered across a dozen apps that don't talk to each other, on devices that consider your data theirs."*

— The Headstone premise

Table of Contents

1. Current Smartphone Data

2. Current Wearables Landscape

3. Emerging Wearables (1–3 Years)

4. Data Richness Assessment

5. Export & Portability

6. The Passive Capture Stack

7. Privacy & Security Considerations

8. Accessibility

9. Summary & Strategic Implications

1. Current Smartphone Data

A modern smartphone is already the most powerful passive life-capture device ever built. Most people carry one 16+ hours per day. The data it generates continuously is staggering.

1.1 Location & Movement

Data Type

Detail Level

Capture Method

|-----------|-------------|----------------|

GPS location	~3m accuracy outdoors	GPS, GLONASS, Galileo chips
Wi-Fi triangulation	~10-15m indoors	Wi-Fi AP fingerprinting
Bluetooth proximity	Room-level	BLE beacons
Cell tower location	~100m-1km	Carrier network
Altitude	Floor-level	Barometric pressure sensor
Speed & acceleration	Continuous	3-axis accelerometer + gyroscope
Step count	Per-step	Pedometer (accelerometer fusion)
Movement patterns	Mode detection	ML on accelerometer (walking, driving, cycling)
Frequent locations	Semantic	OS-level (home, work, gym)
Travel history	Lifetime	Google Timeline / Apple Significant Locations

iOS Export: `CoreLocation`, `CMMotionActivity`, `CMPedometer`

Android Export: `Activity Recognition API`, `Fused Location Provider`, `Google Fit REST API`

Google Takeout: Exports full location history as JSON (Location History.json) — includes every recorded location point with timestamp, accuracy, and inferred activity.

1.2 Communications Patterns (Metadata, Not Content)

Data Type

What It Reveals

API/Access

|-----------|----------------|------------|

Call logs	Who you call, how often, duration, time of day	Native phone APIs
SMS metadata	Message frequency, contacts, timing	Android: `Telephony.Sms`
App communication patterns	Which apps used for messaging	iOS Screen Time / Android Digital Wellbeing
Email metadata	Volume, response times, top contacts	Gmail API, Mail.app
Social graph (inferred)	Who matters to you	Derived from frequency analysis

**Note:** Content of communications is explicitly off-limits for Headstone — metadata only, with full user consent for each category.

1.3 Health & Body Data (Smartphone-Native)

Sensor/Feature

Data Captured

Platform

|---------------|--------------|---------|

Accelerometer	Steps, activity type, sleep motion	iOS + Android
Gyroscope	Balance, gait analysis	iOS + Android
Barometer	Floors climbed, elevation	iOS + Android
Microphone (opt-in)	Ambient noise levels (decibels), sleep sounds	Various apps
Camera	Photo health proxies (skin tone, facial analysis)	Emerging
Face ID / biometrics	Indirectly — facial data for auth (not accessible to apps)	iOS
Crash detection	Fall events, high-impact moments	iPhone 14+, Pixel 8+
Emergency SOS patterns	Stress events	iOS, Android
Menstrual cycle (user-logged)	Cycle tracking	Apple Health, Google Fit

Apple Health data types available via HealthKit API:

• `HKQuantityTypeIdentifierStepCount`
• `HKQuantityTypeIdentifierFlightsClimbed`
• `HKQuantityTypeIdentifierDistanceWalkingRunning`
• `HKQuantityTypeIdentifierBasalEnergyBurned`
• `HKQuantityTypeIdentifierActiveEnergyBurned`
• `HKCategoryTypeIdentifierSleepAnalysis`
• `HKQuantityTypeIdentifierHeartRate` (from paired Apple Watch)
• All third-party app data written to HealthKit store

1.4 App Usage & Digital Behavior

Data Type

Platform API

Export Method

|-----------|-------------|---------------|

This page summarizes the full specification. See the full document for complete details.

Screen time per app	iOS Screen Time API, Android Digital Wellbeing	Limited (iOS restricts 3rd party access); Google Takeout
App open/close times	Available to app developers per-app	Per-app logs